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  <div class="section" id="numpy-histogramdd">
<h1>numpy.histogramdd<a class="headerlink" href="#numpy-histogramdd" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="numpy.histogramdd">
<code class="sig-prename descclassname">numpy.</code><code class="sig-name descname">histogramdd</code><span class="sig-paren">(</span><em class="sig-param">sample</em>, <em class="sig-param">bins=10</em>, <em class="sig-param">range=None</em>, <em class="sig-param">normed=None</em>, <em class="sig-param">weights=None</em>, <em class="sig-param">density=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/numpy/numpy/blob/v1.18.1/numpy/lib/histograms.py#L945-L1123"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#numpy.histogramdd" title="Permalink to this definition">¶</a></dt>
<dd><p>Compute the multidimensional histogram of some data.</p>
<dl class="field-list">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl>
<dt><strong>sample</strong><span class="classifier">(N, D) array, or (D, N) array_like</span></dt><dd><p>The data to be histogrammed.</p>
<p>Note the unusual interpretation of sample when an array_like:</p>
<ul class="simple">
<li><p>When an array, each row is a coordinate in a D-dimensional space -
such as <code class="docutils literal notranslate"><span class="pre">histogramgramdd(np.array([p1,</span> <span class="pre">p2,</span> <span class="pre">p3]))</span></code>.</p></li>
<li><p>When an array_like, each element is the list of values for single
coordinate - such as <code class="docutils literal notranslate"><span class="pre">histogramgramdd((X,</span> <span class="pre">Y,</span> <span class="pre">Z))</span></code>.</p></li>
</ul>
<p>The first form should be preferred.</p>
</dd>
<dt><strong>bins</strong><span class="classifier">sequence or int, optional</span></dt><dd><p>The bin specification:</p>
<ul class="simple">
<li><p>A sequence of arrays describing the monotonically increasing bin
edges along each dimension.</p></li>
<li><p>The number of bins for each dimension (nx, ny, … =bins)</p></li>
<li><p>The number of bins for all dimensions (nx=ny=…=bins).</p></li>
</ul>
</dd>
<dt><strong>range</strong><span class="classifier">sequence, optional</span></dt><dd><p>A sequence of length D, each an optional (lower, upper) tuple giving
the outer bin edges to be used if the edges are not given explicitly in
<em class="xref py py-obj">bins</em>.
An entry of None in the sequence results in the minimum and maximum
values being used for the corresponding dimension.
The default, None, is equivalent to passing a tuple of D None values.</p>
</dd>
<dt><strong>density</strong><span class="classifier">bool, optional</span></dt><dd><p>If False, the default, returns the number of samples in each bin.
If True, returns the probability <em>density</em> function at the bin,
<code class="docutils literal notranslate"><span class="pre">bin_count</span> <span class="pre">/</span> <span class="pre">sample_count</span> <span class="pre">/</span> <span class="pre">bin_volume</span></code>.</p>
</dd>
<dt><strong>normed</strong><span class="classifier">bool, optional</span></dt><dd><p>An alias for the density argument that behaves identically. To avoid
confusion with the broken normed argument to <a class="reference internal" href="numpy.histogram.html#numpy.histogram" title="numpy.histogram"><code class="xref py py-obj docutils literal notranslate"><span class="pre">histogram</span></code></a>, <em class="xref py py-obj">density</em>
should be preferred.</p>
</dd>
<dt><strong>weights</strong><span class="classifier">(N,) array_like, optional</span></dt><dd><p>An array of values <em class="xref py py-obj">w_i</em> weighing each sample <em class="xref py py-obj">(x_i, y_i, z_i, …)</em>.
Weights are normalized to 1 if normed is True. If normed is False,
the values of the returned histogram are equal to the sum of the
weights belonging to the samples falling into each bin.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>H</strong><span class="classifier">ndarray</span></dt><dd><p>The multidimensional histogram of sample x. See normed and weights
for the different possible semantics.</p>
</dd>
<dt><strong>edges</strong><span class="classifier">list</span></dt><dd><p>A list of D arrays describing the bin edges for each dimension.</p>
</dd>
</dl>
</dd>
</dl>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<dl class="simple">
<dt><a class="reference internal" href="numpy.histogram.html#numpy.histogram" title="numpy.histogram"><code class="xref py py-obj docutils literal notranslate"><span class="pre">histogram</span></code></a></dt><dd><p>1-D histogram</p>
</dd>
<dt><a class="reference internal" href="numpy.histogram2d.html#numpy.histogram2d" title="numpy.histogram2d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">histogram2d</span></code></a></dt><dd><p>2-D histogram</p>
</dd>
</dl>
</div>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">r</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span><span class="mi">3</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">H</span><span class="p">,</span> <span class="n">edges</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">histogramdd</span><span class="p">(</span><span class="n">r</span><span class="p">,</span> <span class="n">bins</span> <span class="o">=</span> <span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">H</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">edges</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">size</span><span class="p">,</span> <span class="n">edges</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">size</span><span class="p">,</span> <span class="n">edges</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">size</span>
<span class="go">((5, 8, 4), 6, 9, 5)</span>
</pre></div>
</div>
</dd></dl>

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